devo

Name: devo
Author: 7df-lab

Verified

Open-source, model-neutral agent desktop/runtime for private, enterprise, and OpenAI-compatible model environments. Supports DeepSeek, Qwen, Kimi, Anthropic-compatible APIs, MCP, and local code search.

298stars

129forks

Rust

Installation

# Add to your Claude Code skills
git clone https://github.com/7df-lab/devo

Getting Started

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Security ReportVerified

Last scanned: 6/27/2026

{
  "issues": [
    {
      "file": "README.md",
      "line": 148,
      "type": "remote-install",
      "message": "Install command (remote install script piped to a shell — review the source before running): \"curl -fsSL https://raw.githubusercontent.com/7df-lab/devo/main/install.sh | sh\"",
      "severity": "low"
    }
  ],
  "status": "PASSED",
  "scannedAt": "2026-06-27T06:52:11.481Z",
  "npmAuditRan": true,
  "pipAuditRan": true,
  "promptInjectionRan": true
}

README.md

Frequently Asked Questions

What is devo?

devo is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by 7df-lab. Open-source, model-neutral agent desktop/runtime for private, enterprise, and OpenAI-compatible model environments. Supports DeepSeek, Qwen, Kimi, Anthropic-compatible APIs, MCP, and local code search. It has 298 GitHub stars.

Is devo safe to use?

Yes. devo passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install devo?

Clone the repository with "git clone https://github.com/7df-lab/devo" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is devo written in?

devo is primarily written in Rust. It is open-source under 7df-lab on GitHub, so you can review or fork the full source.

Are there alternatives to devo?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh devo against similar tools.

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An open-source, model-neutral agent desktop/runtime for private, enterprise, and OpenAI-compatible model environments. Connect DeepSeek, Qwen, Kimi, Anthropic-compatible APIs, local gateways, or your own model endpoint.

English | 简体中文 | 繁體中文 | 日本語 | Русский

Why Devo · Features · Tested Models · Tested Platforms · Install · Quick Start · Docs

Why Devo

Devo is for teams that need a coding agent outside a single hosted model ecosystem. It keeps model choice, runtime behavior, and workspace execution under your control.

Bring your own model - Connect OpenAI-compatible Chat Completions, OpenAI-compatible Responses, Anthropic Messages, DeepSeek, Qwen, Kimi, or private model gateways through provider/model bindings.
Works in private and intranet environments - Run a single local Rust binary, support offline installation paths, and point Devo at internal endpoints without depending on a hosted agent service.
Desktop and CLI workflows - Use the Desktop app for onboarding and daily coding, or the CLI/TUI when you need terminal-native automation.
Built for agent runtime extensibility - MCP servers, reusable skills, local semantic code search, auditable sessions, permissions, and multi-agent flows are runtime features rather than one-off prompts.

Features

Built-in semantic code search - Runs a local CPU code-embedding model and combines dense retrieval with BM25 keyword matching, reducing code-search context compared with grep/find-only agent.
Bring your own model provider - Use provider/model bindings for OpenAI-compatible, Anthropic-compatible, DeepSeek, Qwen, Kimi, GLM, MiniMax, Xiaomi MiMo, OpenRouter, or local endpoints.
MCP support - Connect external tools and context through Model Context Protocol servers.
Skill support - Package repeatable workflows, instructions, scripts, and references as reusable Agent Skills.
Long-running task support - Let Devo manage context automatically across multi-turn work instead of losing the thread as tasks grow.
Multi-agent support - Split work across specialized agents while keeping coordination visible in the session.
Plan Mode - Break larger tasks into clear multi-step plans before implementation starts.
Parallel tool calls - Run multiple independent tools at parallel so models spend less time waiting and more time making progress.
Permissioned tool execution - Review sensitive tool calls before they touch your workspace.
Auditable sessions - Keep model output, tool calls, approvals, token usage, and session history inspectable and resumable.
Cost and context visibility - Show input/output tokens, cached tokens, and context-window usage where providers expose them.
Lightweight Rust runtime - Built in Rust with low memory overhead and a compact local runtime.

Tested Models

Devo's built-in model catalog includes tested model definitions for Qwen, Kimi, MiniMax, GLM, and DeepSeek. Provider endpoints remain configurable through provider/model bindings.

Tested Platforms

Devo has been tested on macOS, Linux, Windows, and Kylin OS.

For Chinese Enterprise Users

Kylin OS coverage is called out because domestic operating systems are often part of real deployment requirements in Chinese enterprise environments. HarmonyOS support is on the roadmap; contributors with HarmonyOS devices are welcome to build, test, and publish releases for that platform.

Screenshots

Installation

Devo can be installed in two forms. You can install the Desktop app, the terminal-native TUI/CLI, or both on the same machine.

Option 1: Desktop App

Start here if you want the graphical Devo experience. Download the latest Devo Desktop package from GitHub Releases, then choose the asset that matches your operating system and architecture:

macOS - download the devo-desktop-...-mac-... .dmg or .zip asset.
Windows - download the devo-desktop-...-windows-... .exe asset.
Linux - download the devo-desktop-...-linux-... .AppImage, .deb, or .rpm asset.

If macOS reports that Devo.app is damaged and cannot be opened, this is expected. Current macOS Desktop builds are unsigned, so after installing, run the following command so macOS can launch the app:

sudo xattr -dr com.apple.quarantine /Applications/Devo.app

Option 2: TUI / CLI

Install the terminal-native devo command if you prefer the TUI, want shell automation, or want to use Devo alongside the Desktop app.

Linux / macOS:

curl -fsSL https://raw.githubusercontent.com/7df-lab/devo/main/install.sh | sh

Windows:

irm 'https://raw.githubusercontent.com/7df-lab/devo/main/install.ps1' | iex

The online installer places devo under the Devo home directory, installs the rg sidecar used for fast repository search, and supports optional setup for the local model used by code_search.

Use this only if you want the Hugging Face model downloaded during installation.

Linux / macOS:

curl -fsSL https://raw.githubusercontent.com/7df-lab/devo/main/install.sh | sh -s -- --install-code-search-model

Windows:

$env:DEVO_INSTALL_CODE_SEARCH_MODEL = "1"; irm 'https://raw.githubusercontent.com/7df-lab/devo/main/install.ps1' | iex

Upgrade an existing installation to the latest release:

devo upgrade

The upgrade command runs the same platform installer, and the installer prints the version transition, for example Version: v0.1.12 -> v0.1.15.

For air-gapped or intranet installs, see Offline Installation.

Quick Start

Configure a provider, open a repository, and start the TUI:

cd /path/to/your/repo
devo onboard

Useful commands:

devo                         # start the interactive TUI in the current repo
devo resume <session-id>

Configuration

devo onboard is the recommended setup path. For manual config.toml paths, provider/model binding fields, and custom model catalog examples, see Configuration.

Docs

FAQ

What is the project status?

Devo is pre-1.0 and actively developed. It is ready for local evaluation, experiments, and contributor use; public APIs and configuration may still change.

What models are supported?

Built-in model metadata currently covers Qwen, Kimi, MiniMax, GLM, and DeepSeek families. Any model endpoint that supports OpenAI-compatible Chat Completions, OpenAI-compatible Responses, or the Anthropic Messages API can be connected through provider/model bindings.

Contributing

Contributions are welcome while the project is still early:

Architecture feedback on the client/server runtime, provider layer, safety model, and TUI.
Documentation and translations.